Post-ABS Testing

Load some packages that we’ll need to use to do these calculations:

library(tidyverse)
library(gifski)
library(ggraph)
library(here)
library(igraph)
library(tnet) # for the closeness function described here: https://toreopsahl.com/2010/03/20/closeness-centrality-in-networks-with-disconnected-components/
source(here("modelFunction_rewiring.R"))

1. Run the model once

# Define parameters
N = 50
edge.prob <- 0.04
n.removed = 10
burn.in = 20
recovery = 5
add00 = c(0.5, 10)
lose01 = 0.1
add10 = 0.05
lose11 = c(0.5, 0.5)
coefAdd = 0.5
coefLose = -0.5

modelGraphs <- runModel(N = N, # Nodes in the network
                        edge.prob = edge.prob,
                        n.removed = n.removed,
                        burn.in = burn.in,
                        recovery = recovery,
                        add00 = add00, 
                        lose01 = lose01, 
                        add10 = add10,
                        lose11 = lose11,
                        coefAdd = coefAdd,
                        coefLose = coefLose)$graphs

2. For a single model run, examine network-level measures over time

## Warning: Removed 1 row(s) containing missing values (geom_path).

3. Trends for many model runs

First, I run the model 100 times and compute the network measures for each of the model runs.

Now, I can make some plots to detect general trends in what happens to the network after removal/rewiring.

# Time slice numbers for line placement
back1 <- which(names(modelGraphs) == "back1") 
removed <- which(names(modelGraphs) == "removed")
rewired <- which(names(modelGraphs) == "rewired")

Density behaves the same no matter the number of individuals in the network, just with less variation. To be expected.

## Warning: Removed 500 row(s) containing missing values (geom_path).
## Removed 500 row(s) containing missing values (geom_path).

Mean distance looks like it might have a nonlinear relationship with the number of individuals in the network. Need to investigate this further!

Modularity seems to behave the same no matter the number of individuals in the network.

4. Relationship between removed nodes and network-level measures

## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

A few sanity checks:

What about the ratio between the first and second changes? Aka: what percentage of the loss/gain is recovered by the rewiring?

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning: Removed 883 rows containing non-finite values (stat_smooth).

## Warning: Removed 862 rows containing missing values (geom_point).

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning: Removed 883 rows containing non-finite values (stat_smooth).

## Warning: Removed 862 rows containing missing values (geom_point).

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning in log(ratio): NaNs produced

## Warning: Removed 883 rows containing non-finite values (stat_smooth).

## Warning: Removed 862 rows containing missing values (geom_point).

## Warning: Removed 21 rows containing non-finite values (stat_smooth).

## Warning: Removed 21 rows containing non-finite values (stat_smooth).

## Warning: Removed 21 rows containing non-finite values (stat_smooth).